code: def store_var(name: str, tensor: torch.Tensor):
      if tensor.is_floating_point():
          dims = tuple(i for i in range(len(tensor.shape)))
          tracker.add(f"{name}.mean", tensor.mean())
          var = (tensor**2).mean(dim=dims) - tensor.mean(
              dim=dims
          ) ** 2
          tracker.add(f"{name}.var", var.mean())
  
file_path: labml/client/labml/utils/pytorch.py
imports: from typing import Optional, Dict, TYPE_CHECKING
  import torch
  from labml import tracker
  from labml.configs import BaseConfigs
label_desc: Use image node_id 2 for calling a global function with example usage: store_var(name, tensor) without return types